❯ AI drug developer Aureka Biotechnologies completes $100M Series B

FINANCINGAI drug developer Aureka Biotechnologies has completed a $100 million Series B, with Asia-focused fund Granite Asia exclusively funding the first tranche, an unnamed strategic investor leading the subsequent tranche, and GoTop Capital’s HighLight Capital participating. It works on both platform and pipeline: training the biological foundation model AuraIDE while using its proprietary experimental platform for single-cell functional screening and high-throughput validation to produce antibody molecules. Founded in 2023, with locations in Shanghai and Laguna Hills, California, it has cumulative funding approaching $200 million.

TRANCHESThe structure of this round is worth parsing: the funds close in two tranches, with the first taken up by a single institution and the strategic investor entering only in the second. Less than three years after founding, it has released OpenDDE, the open-source version of AuraIDE, which reportedly outperforms AlphaFold 3 on antibody modeling; commercially it has partnered with multiple multinational pharma companies, and the company says it has booked tens of millions of dollars in revenue over the past two years. At a time when AI-pharma funding has broadly cooled and most companies subsist on milestone payments, closing a $100 million round while keeping a slot for a strategic tranche rests precisely on these two externally verifiable facts — not just a pipeline story.

EDGEAuraIDE’s training data comes from a proprietary protein co-evolution dataset the company built itself; it learns the relationships among sequence, structure, evolution, and function, covering structure modeling, molecule generation, biomolecular interactions, and functional prediction. The difference from most peers: it does not train on public databases — the co-evolution data is self-collected, which sets the model’s ceiling and is also why it can open-source without fear of replication. The other layer is the experimental closed loop: the model produces designs, the proprietary platform runs single-cell functional screening and high-throughput validation, and results flow back into training. The payoffs land on the hardest targets — the company says it has produced differentiated antibodies against GPCRs and bispecific antibodies, molecule classes with extremely low hit rates in conventional methods. The round’s proceeds go mainly to large-scale training of the next-generation model.

RATIONALEWhat this money is pricing is data assets and iteration speed, not a readout from any single drug candidate. Pipeline valuations wait on clinical trials, with timelines measured in years; model capability, in contrast, can be externally verified quarter over quarter. By open-sourcing OpenDDE, Aureka puts that verification in plain sight, then converts it into cash through pharma partnerships. Building teams in both China and the U.S., open-sourcing the model, and closing funding in tranches — these three together constitute a risk structure for the dual uncertainties of geopolitics and R&D. For peers, the frame of reference has shifted from “how many pipelines” to “where the model ranks on public benchmarks and whether drug companies are willing to pay.”

▪ SIGNALTeams in both China and the U.S., an open-sourced model, and capital arriving in tranches — AI drug development’s financing structure is itself an act of risk pricing.